Computer technologies are currently used in dactyloscopy for processing and identification of images (fingerprints and palmprints). Low quality of fingerprints may lead to erroneous image comparison. To avoid errors, images are improved or filtered by means of digital directional filters. Current methods of filtration are only effective for where the curvature of fingerprint lines is low, and ineffective for filtering high-curvature regions.
Digital filter is a mathematical function of two variables that defines a three-dimensional surface. Thus, a filter can have various specific shapes. Filters for improving fingerprint images have one or more parallel ridges, wherein the central ridge is most essential for filtering. This kind of filter acts selectively, that is increases average brightness of pixels in the valley between the ridges and decreases average brightness of pixels in the neighborhood of the ridge.
In practical use of such filter a matrix of coefficients (also known as mask) is developed on the basis of driving function of the filter. This matrix is then applied to the neighborhood of the processed pixel to set its new value of brightness.
The filter is most effective when the orientation of its ridges coincides with the orientation of fingerprint ridges in the area to which the filter is applied. However, the image may have regions in which fingerprint lines have greater curvature, i.e. significantly change their direction along the length of the filter. In this case the filter can only be aligned with the line along a short line section, whereas the rest of the line is oriented differently, and this affects the results of filtration.
Filtration of such regions may result in that false elements emerge on the image, while true elements are lost. To reduce filter-induced image distortions, the filter coverage is usually reduced in the curved. For this reason the sections of greater curvature are poorly filtered.
A method for improving fingerprint image is known from A. Erol, U. Halici, G. Ongun, “Feature Selective Filtering for Ridge Extraction” (c) 1999 by CRC Press LLC. According to Erol et al. an image is processed with a filter adapted (by means of changing its coefficients) to the local properties in the neighborhood of the processed region.
The filter used in this method is an restricted version of the Gabor filter that is designed to give maximum response to ridges at a specific orientation and spacing on the fingerprint image. The impulse response of the filter is given by the product of a Gaussian and a cosine plane wave.
In the spatial domain, when the square variance of the Gaussian is high enough, the filter responds maximally to fingerprint ridges with spacing frequency equal to the magnitude of the wave vector and oriented orthogonal to its direction. The main lobe in the middle matches to the ridge and the side lobes with smaller amplitude correspond to the neighboring ridges.
To adapt the filter to the local properties of the image the following local values are used:                local ridge spacing; and        local ridge orientation.        
The ridge spacing determines both the magnitude of the wave vector and the variance of the Gaussian. The magnitude of the wave vector corresponds to the frequency of the cosine along the wave vector.
Experiments have shown that the filter construction rules stated above work very well when the orientation and ridge spacing are estimated correctly. However errors in the estimation of orientation distorts the image and results in loss of true minutiae or creation of spurious minutiae.
It is usually not possible to estimate orientation correctly neither in the low quality nor in the high-curvature regions of the image. As a solution to the problem, the orientation angle is complemented with a measure of orientation certainty. This orientation certainty factor is used to adjust the variance of the Gaussian. The regions with low orientation certainty are processed using a filter with variance less than those used in high-certainty regions.
Further, analyzing experimental testing of this method, Erol et al. concludes that high-curvature regions of fingerprint lines shows unacceptably low certainty. This results in degradation of filtering efficiency and even incapability to distinguish fingerprint lines in these regions.
Thus, Erol et al. merely provide an automatic method for restricting filtering degree in the regions that are difficult to process, rather than solve the problem of correctly processing high-curvature regions.
The closest prior art to the present invention is a method for improving fingerprint images (U.S. Application No 2005163394, Scholze, 28.06.2005). In this method, image areas are sequentially processed with a directional filter adapted to the local properties of these image areas, the local properties including ridge spacing, fingerprint line orientation and curvature. To determine the local properties, the image is divided into square areas. For each area local properties are then determined. For each point to be processed Gabor filter is used in accordance with the local properties of the image. To adapt the filter to the local curvature, the Fourier transform of the filter is effected, and particularly, the direction of the main axes, as well as the position and size of the bounding ellipse in the frequency domain is changed within the filter bandpass. In the areas where the direction of fingerprint lines substantially changes, the bandpass is increased. This results in decrease of the filter selectivity and, consequently the filter efficiency in the presence of typical artifacts of fingerprint image.